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Videos de Conceptos Relacionados

Types Of Transformers01:16

Types Of Transformers

1.4K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.4K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

511
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
511
Transformers01:26

Transformers

1.7K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.7K
Transformers in Distribution System01:27

Transformers in Distribution System

491
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
491
The Ideal Transformer01:26

The Ideal Transformer

1.4K
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's tangential...
1.4K
Energy Losses in Transformers01:21

Energy Losses in Transformers

1.3K
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
1.3K

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Updated: Jan 14, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

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Transformers de Grafos: Una Encuesta

Ahsan Shehzad, Feng Xia, Shagufta Abid

    IEEE transactions on neural networks and learning systems
    |January 12, 2026
    PubMed
    Resumen
    Este resumen es generado por máquina.

    Los transformadores de grafos combinan el aprendizaje de grafos y los modelos de transformadores para un rendimiento potente en datos de grafos. Esta encuesta revisa su progreso, diseño, aplicaciones y desafíos en el aprendizaje automático.

    Palabras clave:
    aprendizaje automáticoaprendizaje profundoredes neuronalesprocesamiento de lenguaje naturalvisión por computadorateoría de grafostransformadoresaprendizaje de grafosrepresentación de grafosmodelos generativos

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    Last Updated: Jan 14, 2026

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
    04:23

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

    Published on: April 21, 2023

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    Área de la Ciencia:

    • Aprendizaje automático
    • Inteligencia artificial
    • Teoría de grafos

    Sus antecedentes:

    • Los datos estructurados en grafos son prevalentes en muchos dominios.
    • Los modelos tradicionales luchan con relaciones complejas de grafos.
    • Los transformadores sobresalen en la modelización de secuencias, pero necesitan adaptación para grafos.

    Objetivo del estudio:

    • Proporcionar una revisión exhaustiva de los transformadores de grafos.
    • Analizar los principios de diseño y la integración de las características del grafo.
    • Clasificar los modelos de transformadores de grafos existentes e identificar direcciones futuras de investigación.

    Principales métodos:

    • Revisión de conceptos fundamentales en aprendizaje de grafos y transformadores.
    • Análisis de diseños arquitectónicos que integran sesgos inductivos de grafos y atención.
    • Desarrollo de una taxonomía para clasificar transformadores de grafos.
    • Discusión de aplicaciones y desafíos.

    Principales resultados:

    • Los transformadores de grafos muestran un fuerte rendimiento en tareas a nivel de nodo, arista y grafo.
    • Las consideraciones clave de diseño incluyen sesgos inductivos y mecanismos de atención.
    • Se propone una taxonomía basada en la profundidad, la escalabilidad y el preentrenamiento.
    • Los desafíos identificados incluyen la escalabilidad, la robustez y la interpretabilidad.

    Conclusiones:

    • Los transformadores de grafos representan un avance significativo en el aprendizaje automático para datos de grafos.
    • Se necesita más investigación para abordar los desafíos en escalabilidad, generalización e interpretabilidad.
    • El campo tiene un gran potencial para diversas aplicaciones.